Counts of cases and deaths are key metrics of COVID-19 prevalence and burden, and are the basis for model-based estimates and predictions of these statistics. I present here graphs showing these metrics over time in Washington state and a few other USA locations of interest to me. I hope to update the graphs weekly.

See below for caveats and details.

Figure 1a shows case counts per million for Washington state as a whole, the Seattle area where I live, and the adjacent counties to the north and south (Snohomish and Pierce, resp.). Figure 1b shows case counts for Ann Arbor, Boston, San Diego, and Washington DC. The figures use data from Johns Hopkins Center for Systems Science and Engineering (JHU), described below.

In Washington state (Figure 1a), cases are rapidly increasing and are far higher now than earlier in the pandemic. In non-Washington locations (Figure 1b), cases are rapidly increasing everywhere except for a slight decline in Ann Arbor. The decline in Ann Arbor has persisted for two weeks in a row, and while the differences are small, we can hope it’s the start of a downward trend.

Figures 2a-b show deaths per million for the same locations.

The Washington data (Figure 2a) shows three waves. We should know in a week or two whether the peak in the third wave is real or merely reflects reporting delays due to the Thanksgiving holiday. The second peak was thankfully lower than the first; the third is higher than the second. Early peaks dominate the non-Washington data (Figure 2b) making it hard to see details later on, but the rates are heading up. There’s a hint of a flattening (or, at least, slower increase) in Boston. When comparing Figures 2a and 2b, please heed the difference in y-scale: the current Washington rates (10-20 per million) are similar to the non-Washington rates.

The next graphs show the Washington results broken down by age. This data is from Washington State Department of Health (DOH) weekly downloads, described below. An important caveat is that the DOH download systematically undercounts events in recent weeks due to manual curation. See below. In previous versions of this document, I used extrapolation to compensate for the DOH undercounting but have turned it off in this version: performance of the extrapolation became erratic in the most recent data, overshooting some weeks and undershooting others by large amounts. The DOH data in this version is smoothed using a 3-week rolling mean but is otherwise raw.

Figures 3a-d are cases. The graphs are split into 20-year age ranges starting with 0-19, with a final group for 80+.

Early on, the pandemic struck older age groups most heavily. Over time, cases spread into all age groups, even the young. During the second wave, older groups did better in most locations, but counts are now going up rapidly in all age groups and locations. The dip at the end is at least partially an artifact of DOH undercounting (see below)

Figures 4a-d are deaths. These graphs aggregate 0-59 into a single group, since the death rate in these ages is near 0.

The shocking devastation of the 80+ age group early in the pandemic jumps off the page. The early death rate for this group in Seattle (King County) hit almost 700 per million reflecting the early outbreak at a long term care facility in the area. Statewide, the death rate in the 80+ group shows three waves. Deaths in Snohomish (north of Seattle) for the 80+ group peaked early in the pandemic, then declined and stayed fairly low, but now have climbed back up to nearly the first peak; some of the increase reflects an outbreak in a long term care facility in the county. Deaths in Pierce (south of Seattle) for this group are more variable than elsewhere, possibly reflecting delays in reporting deaths to DOH and political turmoil in the county department responsible for reporting this data. The overall rate seems fairly steady at a level near or slightly below the second and third King county peaks.

Caveats

  1. The term case means a person with a detected COVID infection. Washington state limits this to “confirmed cases”, meaning people with positive molecular COVID tests. Some states include “probable cases”, but the data source I use here only includes “confirmed cases” (or so I believe based on the name of the file I download).

  2. Detected cases undercount actual cases by an unknown amount. As testing volume increases over time, it’s reasonable to expect the detected count to get closer to the actual count. Some of the increase in cases we see in the data is due to this artifact. Modelers attempt to correct for this. I don’t include any such corrections here.

  3. The same issues apply to deaths to a lesser extent, except perhaps early in the pandemic.

  4. The geographic granularity in the underlying data is state or county. I refer to locations by city names reasoning that readers are more likely to know “Seattle” or “Ann Arbor” than “King” or “Washtenaw”.

  5. The date granularity in the graphs is weekly. The underlying JHU data is daily; I sum the data by week before graphing.

  6. I smooth the graphs using a 3-week rolling mean for visual appeal. This is especially important for the deaths graphs where the counts are so low that unsmoothed week-to-week variation makes the graphs hard to read.

Data Sources

Washington State Department of Health (DOH)

DOH provides three COVID data streams.

  1. Washington Disease Reporting System (WDRS) provides daily “hot off the presses” results for use by public health officials, health care providers, and qualified researchers. It is not available to the general public, including yours truly.

  2. COVID-19 Data Dashboard provides a web graphical user interface to summary data from WDRS for the general public. (At least, I think the data is from WDRS - they don’t actually say).

  3. Weekly data downloads (available from the Data Dashboard web page) of data curated by DOH staff. The curation corrects errors in the daily feed, such as, duplicate reports, multiple test results for the same incident (e.g., initial and confirmation tests for the same individual), incorrect reporting dates, incorrect county assignments (e.g., when an individual crosses county lines to get tested). DOH updates the weekly data on Sundays.

The weekly downloads lag behind the daily feed causing data for the last few weeks to be incomplete (see below Figures 5a-b). In previous versions of this document I corrected for this undercount by extrapolating data from the preceding six weeks using a linear model (R’s lm). Performance of the extrapolation became erratic in the most recent data, and I’ve turned it off in this version of the document.

The weekly DOH download reports data by age group: 20-year ranges starting with 0-19, with a final group for 80+.

The DOH download includes data on hospital admissions in addition to cases and deaths, although I don’t show this data here.

Issues with DOH Extrapolation

Figures 5a-b compare DOH and JHU cases and deaths for Washington state to illustrate the undercount in the raw DOH data. The discrepancy is sizable in the deaths data.

Johns Hopkins Center for Systems Science and Engineering (JHU)

JHU CSSE has created an impressive portal for COVID data and analysis. They provide their data to the public through a GitHub repository. The data I use is from the csse_covid_19_data/csse_covid_19_time_series directory: time_series_covid19_confirmed_US.csv for cases and time_series_covid19_deaths_US.csv for deaths.

JHU updates the data daily. I usually download the data on Mondays to align with the DOH weekly data drops.

Other Data Sources

I use two other COVID data sources in my project although not in this document.

  • New York Times COVID Repository. The file I download is us-counties.csv. Like Washington DOH and JHU, NYT has county-level data. Unlike these, it includes “probable” as well as “confirmed” cases and deaths; I see no way to separate the two categories.

  • COVID Tracking Project. This project reports a wide range of interesting statistics (negative test counts, for example), but I only use the case and death data. It does not provide county-level data so is not useful for the non-Washington locations I show. The file I download is https://covidtracking.com/data/download/washington-history.csv. I use this only as a check on the state-level Washington data from the other sources.

The population data used for the per capita calculations is from Census Reporter. The file connecting Census Reporter geoids to counties is the Census Bureau Gazetteer.

Comments Please!

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